Sharif University of TechnologyScientia Iranica1026-3098321620260901Impact of measurement error on maximum hybrid exponentially weighted moving average control chart2406210.24200/sci.2021.57789.5417ENMuhammadNoor-ul-AminDepartment of Statistics, COMSATS University Islamabad, Lahore, Pakistan.AmjadJavaidPakistan Bureau of Statistics, Islamabad, Pakistan.MuhammadHanifNational College of Business Administration and Economics, Lahore, Pakistan.Saddam AkberAbbasiDepartment of Mathematics, Qatar University, Doha, Qatar.Journal Article20210222Statistical process control provides various types of control charts for monitoring mean and variance shifts in the industrial production process individually as well as jointly to improve and maintain the quality of products. Authors proposed control charts based on sample values selected to calculate the desired statistics, assuming that these values are measured correctly. But in a real-life situation, measurements of the values may suffer from errors, ultimately affecting the efficiency of control charts. A few of the researchers in the field of control charts also discussed the problem of measurement error during process monitoring and proposed solutions to avoid losses for producers. We also present a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart for joint monitoring of mean and variance, with the effect of measurement error on the efficiency of this control chart, and name it the Maximum Hybrid Exponentially Weighted Moving Average with Measurement Error (Max-HEWMAME) control chart. The impact of measurement error has been shown in the calculations and presented in the form of Average Run Lengths (ARLs) and Standard Deviations of Run Lengths (SDRLs) using the Monte Carlo simulation method. A real-life example is also included to support the simulation results.https://scientiairanica.sharif.edu/article_24062_53ed2b3a7be0b98f96b0c30153c406f3.pdfSharif University of TechnologyScientia Iranica1026-3098321620260901An integrated group entropy-weighted interval type-2 fuzzy weighted aggregated sum product assessment method in maritime transportation2416410.24200/sci.2023.57940.5486ENAmirMohamadghasemiDepartment of Management, Zabol Branch, Islamic Azad University, Zabol, Iran.AbdollahHadi-VenchehDepartment of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.FarhadHosseinzadeh LotfiDepartment of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran.Journal Article20210327This study aims to provide an integrated decision-making approach in maritime transportation problems. The duty of the hatch cover is to barricade the entrance of water into the load container and insulate the material from being damaged. Hence, it has a considerable influence on the efficiency of maritime transportation systems. Since each hatch cover has distinguished properties with respect to criteria compared to the others, the Hatch Cover Evaluation Problem (HCEP) can be considered as a Multi-Criteria Decision-Making (MCDM) problem. In this paper, Interval Type-2 Fuzzy Sets (IT2FSs) are first used to weight criteria and evaluations of hatch covers with respect to the criteria. In addition, an integrated group Shannon entropy-based Weighted Aggregated Sum Product Assessment (WASPAS) approach is applied to solve the HCEP using the Limit Distance Mean (LDM), in which the Interval Type-2 Fuzzy (IT2F) Shannon entropy approach is used to determine the objective weights, and then they are integrated with the subjective weights. On the other hand, in order to demonstrate the effectiveness and practicability of the proposed method, it is illustrated in an example, and the ranked orders are analyzed with the others.https://scientiairanica.sharif.edu/article_24164_ba8f5d54f513cefec05e255b67cbfeee.pdfSharif University of TechnologyScientia Iranica1026-3098321620260901A new artificial neural network approach for time series analysis2417310.24200/sci.2022.58046.5536ENNajmehNeshatIndustrial Engineering, Department, Meybod University, MeybodHashemMahloojiDepartment of Industrial Engineering, Sharif University of Technology, Tehran, Iran.MuratKayaProgram of Industrial Engineering, Sabanci University, Turkey.Journal Article20210404Time series analysis and accurate forecasting of energy prices are critical for both policymakers and market participants. In the practical analysis of price time series, the coefficients play vital roles; however, their accurate estimation is a challenging issue, as they are affected by external factors. This study proposes a new modeling approach for Artificial Neural Networks (ANNs) models based on fuzzy logic. For this purpose, we reformulated an ANN model as a fuzzy Non-Linear Regression (NLR) model to capture the advantages of both fuzzy regression and ANN methodologies. This clear-box model can be applied not only to uncertain, ambiguous, and complex environments, but it is also capable of modeling nonlinear patterns. To illustrate the capability of the proposed approach, we report a case study of Liquefied Natural Gas (LNG) prices in Japan’s market (as one of the world’s largest natural gas importers). The results support that the performance of the proposed approach is acceptable; moreover, it can deal with uncertain and complex environments as a clear-box model.https://scientiairanica.sharif.edu/article_24173_c178a9175169a4607f33d6763420495e.pdfSharif University of TechnologyScientia Iranica1026-3098321620260901Prediction of particulate content in oil based on successive projections algorithm vibration feature selection2422910.24200/sci.2022.58252.5640ENLiuGeSchool of Environmental Engineering, North China Institute of Science and Technology, Hebei, China.ChenBinSchool of Mechanical and Electrical, Hebei Key Laboratory of Safety Monitoring of Mining Equipment, North China Institute of Science and Technology, Hebei, China.Journal Article20210505Aiming at the non-stationary characteristics of oil pressure vibration signals containing particulates, a method for predicting particulate content in oil was proposed based on vibration characteristic frequency extraction by Vibrational Mode Decomposition (VMD), variable selection using the Successive Projections Algorithm (SPA), and T_S fuzzy identification combined. Firstly, the pressure vibration signal was decomposed by VMD, and a series of narrow-band characteristic frequency matrices were obtained. Then, variables were selected using SPA to construct the feature vector matrix. Finally, the feature vector matrix was used as the input to T_S fuzzy identification to identify the content of particulates in oil. The results showed that the VMD reconstruction of the original oil sample pressure signals could well characterize the main variation of the original signal; the 19 variables were selected from the characteristic frequency of the vibration signal from the oil pressure using SPA, the 19 pressure vibration characteristic frequency of 11 sample sets SPA selected was taken as the input variable of T_S identification model; for each set of sample, the predicted output of the content of particulate in oil was obtained, model prediction decision coefficient is 0.8637, the Root Mean Square Error (RMSE) is 0.1979, a reasonable prediction effect was obtained.https://scientiairanica.sharif.edu/article_24229_b33a9a04d42c1cea6f461caa4f68c964.pdfSharif University of TechnologyScientia Iranica1026-3098321620260901A greedy heuristic algorithm to solve a vehicle routing problem-based model for planning and coordinating multiple resources in emergency response to bushfires2425010.24200/sci.2022.57476.5258ENAsudehShahidiDepartment of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.RezaRamezanianDepartment of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.ShahroozShahparvariSchool of Accounting, Information Systems and Supply Chain, College of Business and Law, RMIT University, Melbourne,
Australia.Journal Article20210105Uncoordinated responses are problematic in various operations, especially in crucial circumstances such as bushfires, where they are associated with decreased efficiency, effectiveness, and safety. This paper presents a new Vehicle Routing Problem (VRP) model for routing, scheduling, and coordinating bushfire-related resources, including Ground Resources (GR) and Aerial Resources (ARs) in the suppression phase of a bushfire event. The coordination must be in a way that ARs' operations are allowed prior to GRs' operations; otherwise, aerial interference is unhelpful. The problem is Non deterministic Polynomial time (NP)-hard and cannot be solved in polynomial time using exact methods, but due to the crucial circumstances of bushfires, it should be solved in a reasonable time. Therefore, we propose a greedy heuristic algorithm to solve it. Dividing the bushfire area into a set of fire sites, we solved instances with different numbers of fire sites using both CPLEX and the greedy algorithm and compared the results. CPLEX fails to solve the instances with more than three fire sites, but the greedy algorithm solves the largest instance having 9 fire sites in less than 1 minute. The negligible Relative Percent Difference (RPD) of the greedy algorithm in the first five instances indicates that our proposed algorithm is reliable.https://scientiairanica.sharif.edu/article_24250_eb2dd0ae2304818f615280c7a6b5218a.pdfSharif University of TechnologyScientia Iranica1026-3098321620260901A novel hierarchical dynamic group decision-based fuzzy ranking approach to evaluate green road construction suppliers2425410.24200/sci.2022.58112.5571ENArashBehzadipourDepartment of Civil Engineering, Islamic Azad University, Karaj Branch, Karaj, Iran.HosseinGitinavardFaculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.MohsenAkbarpour ShiraziFaculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran, Iran.Journal Article20210416In recent years, sustainable development and environmental protection have been receiving more attention in construction projects. Hence, the Green Road Construction (GRC) supplier selection problem is key for organizations to improve their environmental and economical performances. Accordingly, a new Hierarchical Group Decision (HGD) fuzzy ranking framework is presented based on Dynamic Interval-Valued Hesitant Fuzzy Numbers (DIVHFN) and the last aggregation approach to select the most appropriate GRC supplier. Thereby, DIVHFN theory and the last aggregation concept could decrease judgmental errors and data loss, respectively. Moreover, the weight of each criterion is obtained by proposing a new Dynamic Interval-Valued Hesitant Fuzzy Maximize Deviation From Ideal Decision (DIVHF-MDFID) method. Furthermore, the experts' weights are determined by presenting a Dynamic Interval-Valued Hesitant Fuzzy Preference Assessment (DIVHF-PA) method. Besides, to obtain precise weights, the opinions of experts are included in criteria/sub-criteria weight computations. Meanwhile, an actual case regarding the GRC supplier evaluation and selection problem for a construction project is provided to demonstrate the implementation process of the proposed approach. Finally, some comparative and sensitivity analyses are performed to confirm the validation and verification of the presented (DIVHF-HGD) approach.https://scientiairanica.sharif.edu/article_24254_66799b2bd0493647b88e462e59d53428.pdfSharif University of TechnologyScientia Iranica1026-3098321620260901An exact iterative algorithm to solve a linear fractional programming problem2426010.24200/sci.2022.58352.5685ENHaleGonce KockenDepartment of Mathematical Engineering, Yildiz Technical University, Istanbul, Turkey.BeyzaAhlatcioglu OzkokDepartment of Business Administration, Yildiz Technical University, Istanbul, Turkey.MehmetAhlatciogluDepartment of Mathematics, Yildiz Technical University, Istanbul, Turkey.0000-0002-3337-6157Journal Article20210603The Linear Fractional Programming (LFP) problem that optimizes the ratio of two linear objective functions under linear constraints has a wide range of application areas. Based on the traditional definition of continuity, we developed an exact iterative algorithm that does not depend on big-M coecients. Removing the nonlinearity in the fractional objective function by converting the objective function into a linear form, an equivalent linear-iterative problem is obtained and a computationally efficient algorithm is proposed. We also analyze the unbounded and asymptotic solution case of the LFP.<br />To demonstrate the efficiency of the proposed method, illustrative numerical examples are provided for all solution cases. Also, we analyze the validity of our algorithm and compare our results with the existing algorithm from the literature by generating random large-scale test problems.https://scientiairanica.sharif.edu/article_24260_d9cb95368c97dd0b139cb4ab8ee367b4.pdf